Neoepitopes arise from somatic mutations in the tumor

Data generation from mouse tumor models

  • Two cell lines (CT26 colon carcinoma and 4T1 breast cancer)
  • Two organs (spleen and tumors)
  • With and without checkpoint inhibitor treatment

Objectives

Elucidation of detected neoepitope characteristics

Method

  • Roadmap of data analysis in the code

Results

Response filtering

  • Response selection criteria: logfold change > 2 and p-value < 0.01

Barracoda selection

  • I dont know how to remove thw white space to make the plot bigger
  • lof fold change of barcode reads across cell lines and treatments

Expression level and rank score

  • Now selction criteria is rank < 2% and expression > 0.1 TPM
  • Can these selection critereia be optimized

Mutation consequence

  • Can mutation type be used to find immunugenic neoepitopes?

Elution, binding affinity rank scores and self-similarity

Improved binding affinity

Mutation position

GGseq logo

Modelling

Discussion

  • Data set is to small to see a clear pattern in immnunugenic and non-immunugenic neoepitopes

Further work:

  • Try out with more data, when more peptides are screened
  • Is the results the same for human data
  • Make packages for human data

R package and shiny app

We have developed barcc package

And a shiny app for rapid visualization of the data

We have developed barcc package, which includes a shiny app for rapid visualization of the data.